Overview

Dataset statistics

Number of variables25
Number of observations33851
Missing cells139238
Missing cells (%)16.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.5 MiB
Average record size in memory200.0 B

Variable types

Categorical15
Unsupported2
Numeric8

Warnings

position has constant value "0" Constant
created_at has constant value "1616604699" Constant
updated_at has constant value "1616604699" Constant
meta has constant value "{ }" Constant
Data as of has constant value "2021-03-24T00:00:00" Constant
Footnote has constant value "One or more data cells have counts between 1-9 and have been suppressed in accordance with NCHS confidentiality standards." Constant
sid has a high cardinality: 33851 distinct values High cardinality
id has a high cardinality: 33851 distinct values High cardinality
State has a high cardinality: 54 distinct values High cardinality
COVID-19 Deaths is highly correlated with Total Deaths and 3 other fieldsHigh correlation
Total Deaths is highly correlated with COVID-19 Deaths and 2 other fieldsHigh correlation
Pneumonia Deaths is highly correlated with COVID-19 Deaths and 4 other fieldsHigh correlation
Pneumonia and COVID-19 Deaths is highly correlated with COVID-19 Deaths and 2 other fieldsHigh correlation
Influenza Deaths is highly correlated with Pneumonia Deaths and 1 other fieldsHigh correlation
Pneumonia, Influenza, or COVID-19 Deaths is highly correlated with COVID-19 Deaths and 4 other fieldsHigh correlation
created_at is highly correlated with Place of Death and 11 other fieldsHigh correlation
Place of Death is highly correlated with created_at and 5 other fieldsHigh correlation
Age group is highly correlated with created_at and 5 other fieldsHigh correlation
updated_at is highly correlated with created_at and 11 other fieldsHigh correlation
Start Date is highly correlated with created_at and 7 other fieldsHigh correlation
Data as of is highly correlated with created_at and 11 other fieldsHigh correlation
End Date is highly correlated with created_at and 7 other fieldsHigh correlation
Footnote is highly correlated with created_at and 11 other fieldsHigh correlation
State is highly correlated with created_at and 5 other fieldsHigh correlation
Group is highly correlated with created_at and 5 other fieldsHigh correlation
Year is highly correlated with created_at and 7 other fieldsHigh correlation
meta is highly correlated with created_at and 11 other fieldsHigh correlation
position is highly correlated with created_at and 11 other fieldsHigh correlation
created_meta has 33851 (100.0%) missing values Missing
updated_meta has 33851 (100.0%) missing values Missing
Year has 4374 (12.9%) missing values Missing
Month has 13122 (38.8%) missing values Missing
COVID-19 Deaths has 6845 (20.2%) missing values Missing
Total Deaths has 5634 (16.6%) missing values Missing
Pneumonia Deaths has 8081 (23.9%) missing values Missing
Pneumonia and COVID-19 Deaths has 6311 (18.6%) missing values Missing
Influenza Deaths has 4789 (14.1%) missing values Missing
Pneumonia, Influenza, or COVID-19 Deaths has 7942 (23.5%) missing values Missing
Footnote has 14438 (42.7%) missing values Missing
COVID-19 Deaths is highly skewed (γ1 = 53.78029524) Skewed
Total Deaths is highly skewed (γ1 = 63.6556105) Skewed
Pneumonia Deaths is highly skewed (γ1 = 54.12139853) Skewed
Pneumonia and COVID-19 Deaths is highly skewed (γ1 = 53.10106605) Skewed
Influenza Deaths is highly skewed (γ1 = 57.09514985) Skewed
Pneumonia, Influenza, or COVID-19 Deaths is highly skewed (γ1 = 54.44722616) Skewed
sid is uniformly distributed Uniform
id is uniformly distributed Uniform
Place of Death is uniformly distributed Uniform
Age group is uniformly distributed Uniform
sid has unique values Unique
id has unique values Unique
created_meta is an unsupported type, check if it needs cleaning or further analysis Unsupported
updated_meta is an unsupported type, check if it needs cleaning or further analysis Unsupported
HHS Region has 1458 (4.3%) zeros Zeros
COVID-19 Deaths has 16751 (49.5%) zeros Zeros
Total Deaths has 6318 (18.7%) zeros Zeros
Pneumonia Deaths has 14905 (44.0%) zeros Zeros
Pneumonia and COVID-19 Deaths has 19891 (58.8%) zeros Zeros
Influenza Deaths has 26864 (79.4%) zeros Zeros
Pneumonia, Influenza, or COVID-19 Deaths has 13417 (39.6%) zeros Zeros

Reproduction

Analysis started2021-04-25 01:13:33.861090
Analysis finished2021-04-25 01:14:04.740263
Duration30.88 seconds
Software versionpandas-profiling v2.11.0
Download configurationconfig.yaml

Variables

sid
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct33851
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size264.6 KiB
row-prz8.4evp-2fvj
 
1
row-5jzy.pay8~2xk8
 
1
row-6gmm-xnbd_87cq
 
1
row-rygv~4wwx.84b7
 
1
row-pkr6~brre-gxzr
 
1
Other values (33846)
33846 

Length

Max length18
Median length18
Mean length18
Min length18

Characters and Unicode

Total characters609318
Distinct characters37
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33851 ?
Unique (%)100.0%

Sample

1st rowrow-xrtt.u63m-petw
2nd rowrow-xvvt_qzkw-rvt2
3rd rowrow-s9xs~pfcz_s4we
4th rowrow-rjn9~8pz5_tcjq
5th rowrow-2ktj.5dff.a4re
ValueCountFrequency (%)
row-prz8.4evp-2fvj1
 
< 0.1%
row-5jzy.pay8~2xk81
 
< 0.1%
row-6gmm-xnbd_87cq1
 
< 0.1%
row-rygv~4wwx.84b71
 
< 0.1%
row-pkr6~brre-gxzr1
 
< 0.1%
row-xira_pge2-gw7r1
 
< 0.1%
row-upwh.4c78.62k71
 
< 0.1%
row-4n4b~wtm6-smnj1
 
< 0.1%
row-jv7p.ubdd-kjpu1
 
< 0.1%
row-x9xi~bk2e_ncce1
 
< 0.1%
Other values (33841)33841
> 99.9%
2021-04-25T02:14:05.551205image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
row-prz8.4evp-2fvj1
 
< 0.1%
row-5jzy.pay8~2xk81
 
< 0.1%
row-6gmm-xnbd_87cq1
 
< 0.1%
row-rygv~4wwx.84b71
 
< 0.1%
row-pkr6~brre-gxzr1
 
< 0.1%
row-xira_pge2-gw7r1
 
< 0.1%
row-upwh.4c78.62k71
 
< 0.1%
row-4n4b~wtm6-smnj1
 
< 0.1%
row-jv7p.ubdd-kjpu1
 
< 0.1%
row-x9xi~bk2e_ncce1
 
< 0.1%
Other values (33841)33841
> 99.9%

Most occurring characters

ValueCountFrequency (%)
-50796
 
8.3%
r46472
 
7.6%
w46394
 
7.6%
o33851
 
5.6%
.17109
 
2.8%
_16954
 
2.8%
~16694
 
2.7%
512980
 
2.1%
912836
 
2.1%
a12836
 
2.1%
Other values (27)342396
56.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter406265
66.7%
Decimal Number101500
 
16.7%
Dash Punctuation50796
 
8.3%
Other Punctuation17109
 
2.8%
Connector Punctuation16954
 
2.8%
Math Symbol16694
 
2.7%

Most frequent character per category

ValueCountFrequency (%)
r46472
 
11.4%
w46394
 
11.4%
o33851
 
8.3%
a12836
 
3.2%
s12802
 
3.2%
i12789
 
3.1%
d12787
 
3.1%
n12779
 
3.1%
z12772
 
3.1%
p12745
 
3.1%
Other values (15)190038
46.8%
ValueCountFrequency (%)
512980
12.8%
912836
12.6%
712736
12.5%
212692
12.5%
412653
12.5%
612634
12.4%
312510
12.3%
812459
12.3%
ValueCountFrequency (%)
-50796
100.0%
ValueCountFrequency (%)
.17109
100.0%
ValueCountFrequency (%)
_16954
100.0%
ValueCountFrequency (%)
~16694
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin406265
66.7%
Common203053
33.3%

Most frequent character per script

ValueCountFrequency (%)
r46472
 
11.4%
w46394
 
11.4%
o33851
 
8.3%
a12836
 
3.2%
s12802
 
3.2%
i12789
 
3.1%
d12787
 
3.1%
n12779
 
3.1%
z12772
 
3.1%
p12745
 
3.1%
Other values (15)190038
46.8%
ValueCountFrequency (%)
-50796
25.0%
.17109
 
8.4%
_16954
 
8.3%
~16694
 
8.2%
512980
 
6.4%
912836
 
6.3%
712736
 
6.3%
212692
 
6.3%
412653
 
6.2%
612634
 
6.2%
Other values (2)24969
12.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII609318
100.0%

Most frequent character per block

ValueCountFrequency (%)
-50796
 
8.3%
r46472
 
7.6%
w46394
 
7.6%
o33851
 
5.6%
.17109
 
2.8%
_16954
 
2.8%
~16694
 
2.7%
512980
 
2.1%
912836
 
2.1%
a12836
 
2.1%
Other values (27)342396
56.2%

id
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct33851
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size264.6 KiB
00000000-0000-0000-9C69-D162DA7C40D8
 
1
00000000-0000-0000-7693-047627873C2B
 
1
00000000-0000-0000-0236-A297A6B7DF43
 
1
00000000-0000-0000-9F4C-F89742DC90FD
 
1
00000000-0000-0000-B099-D367D63CD807
 
1
Other values (33846)
33846 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters1218636
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33851 ?
Unique (%)100.0%

Sample

1st row00000000-0000-0000-985B-3AC768A0E7E1
2nd row00000000-0000-0000-FD08-DDB30B29C9A9
3rd row00000000-0000-0000-DA88-303EA3BF1930
4th row00000000-0000-0000-FA1F-B0B6C8B6BC1C
5th row00000000-0000-0000-E7E4-D4897A0EF3A5
ValueCountFrequency (%)
00000000-0000-0000-9C69-D162DA7C40D81
 
< 0.1%
00000000-0000-0000-7693-047627873C2B1
 
< 0.1%
00000000-0000-0000-0236-A297A6B7DF431
 
< 0.1%
00000000-0000-0000-9F4C-F89742DC90FD1
 
< 0.1%
00000000-0000-0000-B099-D367D63CD8071
 
< 0.1%
00000000-0000-0000-773B-4A596CA3AECE1
 
< 0.1%
00000000-0000-0000-2319-F65C6D803CB01
 
< 0.1%
00000000-0000-0000-112C-DEBA69B25C501
 
< 0.1%
00000000-0000-0000-9D18-EE216807C5521
 
< 0.1%
00000000-0000-0000-06C7-7547BD7E11D31
 
< 0.1%
Other values (33841)33841
> 99.9%
2021-04-25T02:14:06.104347image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
00000000-0000-0000-469f-dd61ac867b601
 
< 0.1%
00000000-0000-0000-68ee-a9208da3f9ce1
 
< 0.1%
00000000-0000-0000-2285-f8cb7e89f2851
 
< 0.1%
00000000-0000-0000-abe1-11854341c1a91
 
< 0.1%
00000000-0000-0000-7112-7a42b25ac4381
 
< 0.1%
00000000-0000-0000-a413-f78970609c761
 
< 0.1%
00000000-0000-0000-da01-f13e70c8a5281
 
< 0.1%
00000000-0000-0000-eff9-2dd0a3a105211
 
< 0.1%
00000000-0000-0000-3f4e-2f3dc1cebed31
 
< 0.1%
00000000-0000-0000-3926-5ddd7c5ef7cd1
 
< 0.1%
Other values (33841)33841
> 99.9%

Most occurring characters

ValueCountFrequency (%)
0575491
47.2%
-135404
 
11.1%
234225
 
2.8%
534087
 
2.8%
834067
 
2.8%
B34023
 
2.8%
134000
 
2.8%
F33845
 
2.8%
733801
 
2.8%
333795
 
2.8%
Other values (7)235898
19.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number880496
72.3%
Uppercase Letter202736
 
16.6%
Dash Punctuation135404
 
11.1%

Most frequent character per category

ValueCountFrequency (%)
0575491
65.4%
234225
 
3.9%
534087
 
3.9%
834067
 
3.9%
134000
 
3.9%
733801
 
3.8%
333795
 
3.8%
633783
 
3.8%
933769
 
3.8%
433478
 
3.8%
ValueCountFrequency (%)
B34023
16.8%
F33845
16.7%
A33776
16.7%
E33751
16.6%
D33741
16.6%
C33600
16.6%
ValueCountFrequency (%)
-135404
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common1015900
83.4%
Latin202736
 
16.6%

Most frequent character per script

ValueCountFrequency (%)
0575491
56.6%
-135404
 
13.3%
234225
 
3.4%
534087
 
3.4%
834067
 
3.4%
134000
 
3.3%
733801
 
3.3%
333795
 
3.3%
633783
 
3.3%
933769
 
3.3%
ValueCountFrequency (%)
B34023
16.8%
F33845
16.7%
A33776
16.7%
E33751
16.6%
D33741
16.6%
C33600
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII1218636
100.0%

Most frequent character per block

ValueCountFrequency (%)
0575491
47.2%
-135404
 
11.1%
234225
 
2.8%
534087
 
2.8%
834067
 
2.8%
B34023
 
2.8%
134000
 
2.8%
F33845
 
2.8%
733801
 
2.8%
333795
 
2.8%
Other values (7)235898
19.4%

position
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size264.6 KiB
0
33851 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters33851
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
033851
100.0%
2021-04-25T02:14:06.444142image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-25T02:14:06.966217image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
033851
100.0%

Most occurring characters

ValueCountFrequency (%)
033851
100.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number33851
100.0%

Most frequent character per category

ValueCountFrequency (%)
033851
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common33851
100.0%

Most frequent character per script

ValueCountFrequency (%)
033851
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII33851
100.0%

Most frequent character per block

ValueCountFrequency (%)
033851
100.0%

created_at
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size264.6 KiB
1616604699
33851 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters338510
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1616604699
2nd row1616604699
3rd row1616604699
4th row1616604699
5th row1616604699
ValueCountFrequency (%)
161660469933851
100.0%
2021-04-25T02:14:07.269352image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-25T02:14:07.365333image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
161660469933851
100.0%

Most occurring characters

ValueCountFrequency (%)
6135404
40.0%
167702
20.0%
967702
20.0%
033851
 
10.0%
433851
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number338510
100.0%

Most frequent character per category

ValueCountFrequency (%)
6135404
40.0%
167702
20.0%
967702
20.0%
033851
 
10.0%
433851
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common338510
100.0%

Most frequent character per script

ValueCountFrequency (%)
6135404
40.0%
167702
20.0%
967702
20.0%
033851
 
10.0%
433851
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII338510
100.0%

Most frequent character per block

ValueCountFrequency (%)
6135404
40.0%
167702
20.0%
967702
20.0%
033851
 
10.0%
433851
 
10.0%

created_meta
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing33851
Missing (%)100.0%
Memory size264.6 KiB

updated_at
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size264.6 KiB
1616604699
33851 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters338510
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1616604699
2nd row1616604699
3rd row1616604699
4th row1616604699
5th row1616604699
ValueCountFrequency (%)
161660469933851
100.0%
2021-04-25T02:14:07.622638image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-25T02:14:07.729009image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
161660469933851
100.0%

Most occurring characters

ValueCountFrequency (%)
6135404
40.0%
167702
20.0%
967702
20.0%
033851
 
10.0%
433851
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number338510
100.0%

Most frequent character per category

ValueCountFrequency (%)
6135404
40.0%
167702
20.0%
967702
20.0%
033851
 
10.0%
433851
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common338510
100.0%

Most frequent character per script

ValueCountFrequency (%)
6135404
40.0%
167702
20.0%
967702
20.0%
033851
 
10.0%
433851
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII338510
100.0%

Most frequent character per block

ValueCountFrequency (%)
6135404
40.0%
167702
20.0%
967702
20.0%
033851
 
10.0%
433851
 
10.0%

updated_meta
Unsupported

MISSING
REJECTED
UNSUPPORTED

Missing33851
Missing (%)100.0%
Memory size264.6 KiB

meta
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size264.6 KiB
{ }
33851 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters101553
Distinct characters3
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row{ }
2nd row{ }
3rd row{ }
4th row{ }
5th row{ }
ValueCountFrequency (%)
{ }33851
100.0%
2021-04-25T02:14:08.017783image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-25T02:14:08.123227image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
67702
100.0%

Most occurring characters

ValueCountFrequency (%)
{33851
33.3%
33851
33.3%
}33851
33.3%

Most occurring categories

ValueCountFrequency (%)
Open Punctuation33851
33.3%
Space Separator33851
33.3%
Close Punctuation33851
33.3%

Most frequent character per category

ValueCountFrequency (%)
{33851
100.0%
ValueCountFrequency (%)
33851
100.0%
ValueCountFrequency (%)
}33851
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common101553
100.0%

Most frequent character per script

ValueCountFrequency (%)
{33851
33.3%
33851
33.3%
}33851
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII101553
100.0%

Most frequent character per block

ValueCountFrequency (%)
{33851
33.3%
33851
33.3%
}33851
33.3%

Data as of
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size264.6 KiB
2021-03-24T00:00:00
33851 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters643169
Distinct characters8
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-03-24T00:00:00
2nd row2021-03-24T00:00:00
3rd row2021-03-24T00:00:00
4th row2021-03-24T00:00:00
5th row2021-03-24T00:00:00
ValueCountFrequency (%)
2021-03-24T00:00:0033851
100.0%
2021-04-25T02:14:08.455243image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-25T02:14:08.602498image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
2021-03-24t00:00:0033851
100.0%

Most occurring characters

ValueCountFrequency (%)
0270808
42.1%
2101553
 
15.8%
-67702
 
10.5%
:67702
 
10.5%
133851
 
5.3%
333851
 
5.3%
433851
 
5.3%
T33851
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number473914
73.7%
Dash Punctuation67702
 
10.5%
Other Punctuation67702
 
10.5%
Uppercase Letter33851
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
0270808
57.1%
2101553
 
21.4%
133851
 
7.1%
333851
 
7.1%
433851
 
7.1%
ValueCountFrequency (%)
-67702
100.0%
ValueCountFrequency (%)
T33851
100.0%
ValueCountFrequency (%)
:67702
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common609318
94.7%
Latin33851
 
5.3%

Most frequent character per script

ValueCountFrequency (%)
0270808
44.4%
2101553
 
16.7%
-67702
 
11.1%
:67702
 
11.1%
133851
 
5.6%
333851
 
5.6%
433851
 
5.6%
ValueCountFrequency (%)
T33851
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII643169
100.0%

Most frequent character per block

ValueCountFrequency (%)
0270808
42.1%
2101553
 
15.8%
-67702
 
10.5%
:67702
 
10.5%
133851
 
5.3%
333851
 
5.3%
433851
 
5.3%
T33851
 
5.3%

Start Date
Categorical

HIGH CORRELATION

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size264.6 KiB
2020-01-01T00:00:00
10197 
2021-01-01T00:00:00
5751 
2020-08-01T00:00:00
1379 
2020-11-01T00:00:00
 
1377
2020-05-01T00:00:00
 
1377
Other values (10)
13770 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters643169
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-01-01T00:00:00
2nd row2020-01-01T00:00:00
3rd row2020-01-01T00:00:00
4th row2020-01-01T00:00:00
5th row2020-01-01T00:00:00
ValueCountFrequency (%)
2020-01-01T00:00:0010197
30.1%
2021-01-01T00:00:005751
17.0%
2020-08-01T00:00:001379
 
4.1%
2020-11-01T00:00:001377
 
4.1%
2020-05-01T00:00:001377
 
4.1%
2020-12-01T00:00:001377
 
4.1%
2021-03-01T00:00:001377
 
4.1%
2020-10-01T00:00:001377
 
4.1%
2020-07-01T00:00:001377
 
4.1%
2020-04-01T00:00:001377
 
4.1%
Other values (5)6885
20.3%
2021-04-25T02:14:08.897502image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2020-01-01t00:00:0010197
30.1%
2021-01-01t00:00:005751
17.0%
2020-08-01t00:00:001379
 
4.1%
2021-02-01t00:00:001377
 
4.1%
2020-07-01t00:00:001377
 
4.1%
2020-09-01t00:00:001377
 
4.1%
2020-11-01t00:00:001377
 
4.1%
2020-10-01t00:00:001377
 
4.1%
2020-05-01t00:00:001377
 
4.1%
2020-06-01t00:00:001377
 
4.1%
Other values (5)6885
20.3%

Most occurring characters

ValueCountFrequency (%)
0327251
50.9%
271833
 
11.2%
-67702
 
10.5%
:67702
 
10.5%
163812
 
9.9%
T33851
 
5.3%
32754
 
0.4%
81379
 
0.2%
41377
 
0.2%
51377
 
0.2%
Other values (3)4131
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number473914
73.7%
Dash Punctuation67702
 
10.5%
Other Punctuation67702
 
10.5%
Uppercase Letter33851
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
0327251
69.1%
271833
 
15.2%
163812
 
13.5%
32754
 
0.6%
81379
 
0.3%
41377
 
0.3%
51377
 
0.3%
61377
 
0.3%
71377
 
0.3%
91377
 
0.3%
ValueCountFrequency (%)
-67702
100.0%
ValueCountFrequency (%)
T33851
100.0%
ValueCountFrequency (%)
:67702
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common609318
94.7%
Latin33851
 
5.3%

Most frequent character per script

ValueCountFrequency (%)
0327251
53.7%
271833
 
11.8%
-67702
 
11.1%
:67702
 
11.1%
163812
 
10.5%
32754
 
0.5%
81379
 
0.2%
41377
 
0.2%
51377
 
0.2%
61377
 
0.2%
Other values (2)2754
 
0.5%
ValueCountFrequency (%)
T33851
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII643169
100.0%

Most frequent character per block

ValueCountFrequency (%)
0327251
50.9%
271833
 
11.2%
-67702
 
10.5%
:67702
 
10.5%
163812
 
9.9%
T33851
 
5.3%
32754
 
0.4%
81379
 
0.2%
41377
 
0.2%
51377
 
0.2%
Other values (3)4131
 
0.6%

End Date
Categorical

HIGH CORRELATION

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size264.6 KiB
2021-03-20T00:00:00
10125 
2020-12-31T00:00:00
5751 
2020-01-31T00:00:00
1449 
2020-08-31T00:00:00
1379 
2020-10-31T00:00:00
 
1377
Other values (10)
13770 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters643169
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021-03-20T00:00:00
2nd row2021-03-20T00:00:00
3rd row2021-03-20T00:00:00
4th row2021-03-20T00:00:00
5th row2021-03-20T00:00:00
ValueCountFrequency (%)
2021-03-20T00:00:0010125
29.9%
2020-12-31T00:00:005751
17.0%
2020-01-31T00:00:001449
 
4.3%
2020-08-31T00:00:001379
 
4.1%
2020-10-31T00:00:001377
 
4.1%
2020-11-30T00:00:001377
 
4.1%
2020-09-30T00:00:001377
 
4.1%
2020-04-30T00:00:001377
 
4.1%
2020-05-31T00:00:001377
 
4.1%
2021-02-28T00:00:001377
 
4.1%
Other values (5)6885
20.3%
2021-04-25T02:14:09.347289image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021-03-20t00:00:0010125
29.9%
2020-12-31t00:00:005751
17.0%
2020-01-31t00:00:001449
 
4.3%
2020-08-31t00:00:001379
 
4.1%
2020-04-30t00:00:001377
 
4.1%
2020-11-30t00:00:001377
 
4.1%
2021-02-28t00:00:001377
 
4.1%
2020-06-30t00:00:001377
 
4.1%
2020-07-31t00:00:001377
 
4.1%
2020-02-29t00:00:001377
 
4.1%
Other values (5)6885
20.3%

Most occurring characters

ValueCountFrequency (%)
0300285
46.7%
289086
 
13.9%
-67702
 
10.5%
:67702
 
10.5%
141051
 
6.4%
T33851
 
5.3%
332474
 
5.0%
82756
 
0.4%
92754
 
0.4%
41377
 
0.2%
Other values (3)4131
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number473914
73.7%
Dash Punctuation67702
 
10.5%
Other Punctuation67702
 
10.5%
Uppercase Letter33851
 
5.3%

Most frequent character per category

ValueCountFrequency (%)
0300285
63.4%
289086
 
18.8%
141051
 
8.7%
332474
 
6.9%
82756
 
0.6%
92754
 
0.6%
41377
 
0.3%
51377
 
0.3%
61377
 
0.3%
71377
 
0.3%
ValueCountFrequency (%)
-67702
100.0%
ValueCountFrequency (%)
T33851
100.0%
ValueCountFrequency (%)
:67702
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common609318
94.7%
Latin33851
 
5.3%

Most frequent character per script

ValueCountFrequency (%)
0300285
49.3%
289086
 
14.6%
-67702
 
11.1%
:67702
 
11.1%
141051
 
6.7%
332474
 
5.3%
82756
 
0.5%
92754
 
0.5%
41377
 
0.2%
51377
 
0.2%
Other values (2)2754
 
0.5%
ValueCountFrequency (%)
T33851
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII643169
100.0%

Most frequent character per block

ValueCountFrequency (%)
0300285
46.7%
289086
 
13.9%
-67702
 
10.5%
:67702
 
10.5%
141051
 
6.4%
T33851
 
5.3%
332474
 
5.0%
82756
 
0.4%
92754
 
0.4%
41377
 
0.2%
Other values (3)4131
 
0.6%

Group
Categorical

HIGH CORRELATION

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size264.6 KiB
By Month
20729 
By Year
8748 
By Total
4374 

Length

Max length8
Median length8
Mean length7.741573366
Min length7

Characters and Unicode

Total characters262060
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBy Total
2nd rowBy Total
3rd rowBy Total
4th rowBy Total
5th rowBy Total
ValueCountFrequency (%)
By Month20729
61.2%
By Year8748
25.8%
By Total4374
 
12.9%
2021-04-25T02:14:09.899441image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-25T02:14:10.010375image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
by33851
50.0%
month20729
30.6%
year8748
 
12.9%
total4374
 
6.5%

Most occurring characters

ValueCountFrequency (%)
B33851
12.9%
y33851
12.9%
33851
12.9%
o25103
9.6%
t25103
9.6%
M20729
7.9%
n20729
7.9%
h20729
7.9%
a13122
 
5.0%
Y8748
 
3.3%
Other values (4)26244
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter160507
61.2%
Uppercase Letter67702
25.8%
Space Separator33851
 
12.9%

Most frequent character per category

ValueCountFrequency (%)
y33851
21.1%
o25103
15.6%
t25103
15.6%
n20729
12.9%
h20729
12.9%
a13122
 
8.2%
e8748
 
5.5%
r8748
 
5.5%
l4374
 
2.7%
ValueCountFrequency (%)
B33851
50.0%
M20729
30.6%
Y8748
 
12.9%
T4374
 
6.5%
ValueCountFrequency (%)
33851
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin228209
87.1%
Common33851
 
12.9%

Most frequent character per script

ValueCountFrequency (%)
B33851
14.8%
y33851
14.8%
o25103
11.0%
t25103
11.0%
M20729
9.1%
n20729
9.1%
h20729
9.1%
a13122
 
5.7%
Y8748
 
3.8%
e8748
 
3.8%
Other values (3)17496
7.7%
ValueCountFrequency (%)
33851
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII262060
100.0%

Most frequent character per block

ValueCountFrequency (%)
B33851
12.9%
y33851
12.9%
33851
12.9%
o25103
9.6%
t25103
9.6%
M20729
7.9%
n20729
7.9%
h20729
7.9%
a13122
 
5.0%
Y8748
 
3.3%
Other values (4)26244
10.0%

Year
Categorical

HIGH CORRELATION
MISSING

Distinct2
Distinct (%)< 0.1%
Missing4374
Missing (%)12.9%
Memory size264.6 KiB
2020.0
20972 
2021.0
8505 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters176862
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020.0
2nd row2020.0
3rd row2020.0
4th row2020.0
5th row2020.0
ValueCountFrequency (%)
2020.020972
62.0%
2021.08505
25.1%
(Missing)4374
 
12.9%
2021-04-25T02:14:10.312848image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-25T02:14:10.410354image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
2020.020972
71.1%
2021.08505
28.9%

Most occurring characters

ValueCountFrequency (%)
079926
45.2%
258954
33.3%
.29477
 
16.7%
18505
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number147385
83.3%
Other Punctuation29477
 
16.7%

Most frequent character per category

ValueCountFrequency (%)
079926
54.2%
258954
40.0%
18505
 
5.8%
ValueCountFrequency (%)
.29477
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common176862
100.0%

Most frequent character per script

ValueCountFrequency (%)
079926
45.2%
258954
33.3%
.29477
 
16.7%
18505
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII176862
100.0%

Most frequent character per block

ValueCountFrequency (%)
079926
45.2%
258954
33.3%
.29477
 
16.7%
18505
 
4.8%

Month
Real number (ℝ≥0)

MISSING

Distinct12
Distinct (%)0.1%
Missing13122
Missing (%)38.8%
Infinite0
Infinite (%)0.0%
Mean5.584253944
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size264.6 KiB
2021-04-25T02:14:10.522440image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q39
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.596528922
Coefficient of variation (CV)0.6440482396
Kurtosis-1.236584391
Mean5.584253944
Median Absolute Deviation (MAD)3
Skewness0.3390501768
Sum115756
Variance12.93502029
MonotocityNot monotonic
2021-04-25T02:14:10.665040image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12826
 
8.3%
32754
 
8.1%
22754
 
8.1%
81379
 
4.1%
71377
 
4.1%
121377
 
4.1%
61377
 
4.1%
101377
 
4.1%
111377
 
4.1%
51377
 
4.1%
Other values (2)2754
 
8.1%
(Missing)13122
38.8%
ValueCountFrequency (%)
12826
8.3%
22754
8.1%
32754
8.1%
41377
4.1%
51377
4.1%
ValueCountFrequency (%)
121377
4.1%
111377
4.1%
101377
4.1%
91377
4.1%
81379
4.1%

HHS Region
Real number (ℝ≥0)

ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.47732711
Minimum0
Maximum10
Zeros1458
Zeros (%)4.3%
Memory size264.6 KiB
2021-04-25T02:14:10.842398image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median5
Q38
95-th percentile10
Maximum10
Range10
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.956540698
Coefficient of variation (CV)0.5397780045
Kurtosis-1.082835947
Mean5.47732711
Median Absolute Deviation (MAD)2
Skewness-0.03489290778
Sum185413
Variance8.741132897
MonotocityNot monotonic
2021-04-25T02:14:11.010032image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
45589
16.5%
94617
13.6%
53890
11.5%
33888
11.5%
103402
10.0%
12677
7.9%
82673
7.9%
62430
7.2%
72255
6.7%
01458
 
4.3%
ValueCountFrequency (%)
01458
 
4.3%
12677
7.9%
2972
 
2.9%
33888
11.5%
45589
16.5%
ValueCountFrequency (%)
103402
10.0%
94617
13.6%
82673
7.9%
72255
6.7%
62430
7.2%

State
Categorical

HIGH CARDINALITY
HIGH CORRELATION

Distinct54
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size264.6 KiB
Florida
 
1458
United States
 
1458
California
 
1458
Iowa
 
1458
Alaska
 
1458
Other values (49)
26561 

Length

Max length20
Median length8
Mean length8.548669168
Min length4

Characters and Unicode

Total characters289381
Distinct characters46
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnited States
2nd rowUnited States
3rd rowUnited States
4th rowUnited States
5th rowUnited States
ValueCountFrequency (%)
Florida1458
 
4.3%
United States1458
 
4.3%
California1458
 
4.3%
Iowa1458
 
4.3%
Alaska1458
 
4.3%
Connecticut1458
 
4.3%
Idaho1458
 
4.3%
Indiana1458
 
4.3%
Arizona1458
 
4.3%
Alabama1458
 
4.3%
Other values (44)19271
56.9%
2021-04-25T02:14:11.628939image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
connecticut1458
 
3.5%
hawaii1458
 
3.5%
iowa1458
 
3.5%
georgia1458
 
3.5%
columbia1458
 
3.5%
united1458
 
3.5%
district1458
 
3.5%
delaware1458
 
3.5%
indiana1458
 
3.5%
arizona1458
 
3.5%
Other values (50)26804
64.8%

Most occurring characters

ValueCountFrequency (%)
a42418
14.7%
i29164
 
10.1%
o25035
 
8.7%
n19759
 
6.8%
r15556
 
5.4%
s15206
 
5.3%
e15070
 
5.2%
l14580
 
5.0%
t14341
 
5.0%
d8262
 
2.9%
Other values (36)89990
31.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter241922
83.6%
Uppercase Letter39926
 
13.8%
Space Separator7533
 
2.6%

Most frequent character per category

ValueCountFrequency (%)
a42418
17.5%
i29164
12.1%
o25035
10.3%
n19759
8.2%
r15556
 
6.4%
s15206
 
6.3%
e15070
 
6.2%
l14580
 
6.0%
t14341
 
5.9%
d8262
 
3.4%
Other values (14)42531
17.6%
ValueCountFrequency (%)
C6561
16.4%
I6075
15.2%
A5832
14.6%
D3402
8.5%
M2187
 
5.5%
N2187
 
5.5%
S1944
 
4.9%
U1701
 
4.3%
H1701
 
4.3%
F1458
 
3.7%
Other values (11)6878
17.2%
ValueCountFrequency (%)
7533
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin281848
97.4%
Common7533
 
2.6%

Most frequent character per script

ValueCountFrequency (%)
a42418
15.0%
i29164
 
10.3%
o25035
 
8.9%
n19759
 
7.0%
r15556
 
5.5%
s15206
 
5.4%
e15070
 
5.3%
l14580
 
5.2%
t14341
 
5.1%
d8262
 
2.9%
Other values (35)82457
29.3%
ValueCountFrequency (%)
7533
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII289381
100.0%

Most frequent character per block

ValueCountFrequency (%)
a42418
14.7%
i29164
 
10.1%
o25035
 
8.7%
n19759
 
6.8%
r15556
 
5.4%
s15206
 
5.3%
e15070
 
5.2%
l14580
 
5.0%
t14341
 
5.0%
d8262
 
2.9%
Other values (36)89990
31.1%

Place of Death
Categorical

HIGH CORRELATION
UNIFORM

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size264.6 KiB
Healthcare setting, outpatient or emergency room
3762 
Nursing home/long term care facility
3762 
Total - All Places of Death
3762 
Healthcare setting, dead on arrival
3762 
Hospice facility
3762 
Other values (4)
15041 

Length

Max length48
Median length27
Mean length25.89069747
Min length5

Characters and Unicode

Total characters876426
Distinct characters33
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTotal - All Places of Death
2nd rowTotal - All Places of Death
3rd rowTotal - All Places of Death
4th rowTotal - All Places of Death
5th rowTotal - All Places of Death
ValueCountFrequency (%)
Healthcare setting, outpatient or emergency room3762
11.1%
Nursing home/long term care facility3762
11.1%
Total - All Places of Death3762
11.1%
Healthcare setting, dead on arrival3762
11.1%
Hospice facility3762
11.1%
Decedent's home3762
11.1%
Healthcare setting, inpatient3762
11.1%
Other3760
11.1%
Place of death unknown3757
11.1%
2021-04-25T02:14:12.103418image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-25T02:14:12.238652image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
healthcare11286
 
8.8%
setting11286
 
8.8%
facility7524
 
5.9%
of7519
 
5.9%
death7519
 
5.9%
dead3762
 
2.9%
inpatient3762
 
2.9%
or3762
 
2.9%
arrival3762
 
2.9%
decedent's3762
 
2.9%
Other values (17)63942
50.0%

Most occurring characters

ValueCountFrequency (%)
e105324
12.0%
94035
 
10.7%
t82757
 
9.4%
a71468
 
8.2%
n52653
 
6.0%
i48906
 
5.6%
o48896
 
5.6%
r45142
 
5.2%
l45139
 
5.2%
c41377
 
4.7%
Other values (23)240729
27.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter714682
81.5%
Space Separator94035
 
10.7%
Uppercase Letter45137
 
5.2%
Other Punctuation18810
 
2.1%
Dash Punctuation3762
 
0.4%

Most frequent character per category

ValueCountFrequency (%)
e105324
14.7%
t82757
11.6%
a71468
10.0%
n52653
 
7.4%
i48906
 
6.8%
o48896
 
6.8%
r45142
 
6.3%
l45139
 
6.3%
c41377
 
5.8%
h30089
 
4.2%
Other values (11)142931
20.0%
ValueCountFrequency (%)
H15048
33.3%
D7524
16.7%
P7519
16.7%
T3762
 
8.3%
A3762
 
8.3%
N3762
 
8.3%
O3760
 
8.3%
ValueCountFrequency (%)
,11286
60.0%
'3762
 
20.0%
/3762
 
20.0%
ValueCountFrequency (%)
94035
100.0%
ValueCountFrequency (%)
-3762
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin759819
86.7%
Common116607
 
13.3%

Most frequent character per script

ValueCountFrequency (%)
e105324
13.9%
t82757
10.9%
a71468
 
9.4%
n52653
 
6.9%
i48906
 
6.4%
o48896
 
6.4%
r45142
 
5.9%
l45139
 
5.9%
c41377
 
5.4%
h30089
 
4.0%
Other values (18)188068
24.8%
ValueCountFrequency (%)
94035
80.6%
,11286
 
9.7%
-3762
 
3.2%
'3762
 
3.2%
/3762
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII876426
100.0%

Most frequent character per block

ValueCountFrequency (%)
e105324
12.0%
94035
 
10.7%
t82757
 
9.4%
a71468
 
8.2%
n52653
 
6.0%
i48906
 
5.6%
o48896
 
5.6%
r45142
 
5.2%
l45139
 
5.2%
c41377
 
4.7%
Other values (23)240729
27.5%

Age group
Categorical

HIGH CORRELATION
UNIFORM

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size264.6 KiB
18-29 years
3762 
40-49 years
3762 
0-17 years
3762 
30-39 years
3761 
65-74 years
3761 
Other values (4)
15043 

Length

Max length17
Median length11
Mean length11.2220023
Min length8

Characters and Unicode

Total characters379876
Distinct characters24
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAll Ages
2nd row0-17 years
3rd row18-29 years
4th row30-39 years
5th row40-49 years
ValueCountFrequency (%)
18-29 years3762
11.1%
40-49 years3762
11.1%
0-17 years3762
11.1%
30-39 years3761
11.1%
65-74 years3761
11.1%
75-84 years3761
11.1%
50-64 years3761
11.1%
All Ages3761
11.1%
85 years and over3760
11.1%
2021-04-25T02:14:12.636007image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-25T02:14:12.798193image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
years30090
40.0%
40-493762
 
5.0%
0-173762
 
5.0%
18-293762
 
5.0%
30-393761
 
5.0%
ages3761
 
5.0%
all3761
 
5.0%
75-843761
 
5.0%
50-643761
 
5.0%
65-743761
 
5.0%
Other values (3)11280
 
15.0%

Most occurring characters

ValueCountFrequency (%)
41371
10.9%
e37611
9.9%
s33851
 
8.9%
a33850
 
8.9%
r33850
 
8.9%
y30090
 
7.9%
-26330
 
6.9%
418807
 
5.0%
015046
 
4.0%
515043
 
4.0%
Other values (14)94027
24.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter195575
51.5%
Decimal Number109078
28.7%
Space Separator41371
 
10.9%
Dash Punctuation26330
 
6.9%
Uppercase Letter7522
 
2.0%

Most frequent character per category

ValueCountFrequency (%)
e37611
19.2%
s33851
17.3%
a33850
17.3%
r33850
17.3%
y30090
15.4%
l7522
 
3.8%
g3761
 
1.9%
n3760
 
1.9%
d3760
 
1.9%
o3760
 
1.9%
ValueCountFrequency (%)
418807
17.2%
015046
13.8%
515043
13.8%
911285
10.3%
711284
10.3%
811283
10.3%
17524
6.9%
37522
 
6.9%
67522
 
6.9%
23762
 
3.4%
ValueCountFrequency (%)
A7522
100.0%
ValueCountFrequency (%)
41371
100.0%
ValueCountFrequency (%)
-26330
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin203097
53.5%
Common176779
46.5%

Most frequent character per script

ValueCountFrequency (%)
e37611
18.5%
s33851
16.7%
a33850
16.7%
r33850
16.7%
y30090
14.8%
A7522
 
3.7%
l7522
 
3.7%
g3761
 
1.9%
n3760
 
1.9%
d3760
 
1.9%
Other values (2)7520
 
3.7%
ValueCountFrequency (%)
41371
23.4%
-26330
14.9%
418807
10.6%
015046
 
8.5%
515043
 
8.5%
911285
 
6.4%
711284
 
6.4%
811283
 
6.4%
17524
 
4.3%
37522
 
4.3%
Other values (2)11284
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII379876
100.0%

Most frequent character per block

ValueCountFrequency (%)
41371
10.9%
e37611
9.9%
s33851
 
8.9%
a33850
 
8.9%
r33850
 
8.9%
y30090
 
7.9%
-26330
 
6.9%
418807
 
5.0%
015046
 
4.0%
515043
 
4.0%
Other values (14)94027
24.8%

COVID-19 Deaths
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct2058
Distinct (%)7.6%
Missing6845
Missing (%)20.2%
Infinite0
Infinite (%)0.0%
Mean417.9198326
Minimum0
Maximum526027
Zeros16751
Zeros (%)49.5%
Memory size264.6 KiB
2021-04-25T02:14:13.015249image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q336
95-th percentile990
Maximum526027
Range526027
Interquartile range (IQR)36

Descriptive statistics

Standard deviation5777.60144
Coefficient of variation (CV)13.82466442
Kurtosis3882.766281
Mean417.9198326
Median Absolute Deviation (MAD)0
Skewness53.78029524
Sum11286343
Variance33380678.4
MonotocityNot monotonic
2021-04-25T02:14:13.289523image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
016751
49.5%
11248
 
0.7%
10247
 
0.7%
13204
 
0.6%
12201
 
0.6%
14198
 
0.6%
16167
 
0.5%
15159
 
0.5%
17152
 
0.4%
18139
 
0.4%
Other values (2048)8540
25.2%
(Missing)6845
20.2%
ValueCountFrequency (%)
016751
49.5%
170
 
0.2%
254
 
0.2%
340
 
0.1%
427
 
0.1%
ValueCountFrequency (%)
5260271
< 0.1%
3790301
< 0.1%
3422591
< 0.1%
2410701
< 0.1%
1625831
< 0.1%

Total Deaths
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct4927
Distinct (%)17.5%
Missing5634
Missing (%)16.6%
Infinite0
Infinite (%)0.0%
Mean3079.592763
Minimum0
Maximum4035809
Zeros6318
Zeros (%)18.7%
Memory size264.6 KiB
2021-04-25T02:14:14.149211image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112
median84
Q3594
95-th percentile8180.6
Maximum4035809
Range4035809
Interquartile range (IQR)582

Descriptive statistics

Standard deviation40345.29074
Coefficient of variation (CV)13.10085256
Kurtosis5442.237732
Mean3079.592763
Median Absolute Deviation (MAD)84
Skewness63.6556105
Sum86896869
Variance1627742485
MonotocityNot monotonic
2021-04-25T02:14:14.564481image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06318
 
18.7%
10301
 
0.9%
11258
 
0.8%
12256
 
0.8%
15214
 
0.6%
13212
 
0.6%
14206
 
0.6%
16177
 
0.5%
17169
 
0.5%
19168
 
0.5%
Other values (4917)19938
58.9%
(Missing)5634
 
16.6%
ValueCountFrequency (%)
06318
18.7%
110
 
< 0.1%
28
 
< 0.1%
311
 
< 0.1%
410
 
< 0.1%
ValueCountFrequency (%)
40358091
< 0.1%
33664801
< 0.1%
13393391
< 0.1%
12570531
< 0.1%
12082361
< 0.1%

Pneumonia Deaths
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct1911
Distinct (%)7.4%
Missing8081
Missing (%)23.9%
Infinite0
Infinite (%)0.0%
Mean379.6217307
Minimum0
Maximum453484
Zeros14905
Zeros (%)44.0%
Memory size264.6 KiB
2021-04-25T02:14:14.811091image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q342
95-th percentile919.55
Maximum453484
Range453484
Interquartile range (IQR)42

Descriptive statistics

Standard deviation5275.495561
Coefficient of variation (CV)13.89671648
Kurtosis3762.484959
Mean379.6217307
Median Absolute Deviation (MAD)0
Skewness54.12139853
Sum9782852
Variance27830853.42
MonotocityNot monotonic
2021-04-25T02:14:15.591732image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
014905
44.0%
10331
 
1.0%
11287
 
0.8%
13238
 
0.7%
12236
 
0.7%
14206
 
0.6%
15191
 
0.6%
16189
 
0.6%
19182
 
0.5%
18181
 
0.5%
Other values (1901)8824
26.1%
(Missing)8081
23.9%
ValueCountFrequency (%)
014905
44.0%
164
 
0.2%
257
 
0.2%
338
 
0.1%
430
 
0.1%
ValueCountFrequency (%)
4534841
< 0.1%
3484591
< 0.1%
3365251
< 0.1%
2538301
< 0.1%
1280211
< 0.1%

Pneumonia and COVID-19 Deaths
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct1452
Distinct (%)5.3%
Missing6311
Missing (%)18.6%
Infinite0
Infinite (%)0.0%
Mean200.3193174
Minimum0
Maximum255848
Zeros19891
Zeros (%)58.8%
Memory size264.6 KiB
2021-04-25T02:14:16.417777image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q312
95-th percentile422
Maximum255848
Range255848
Interquartile range (IQR)12

Descriptive statistics

Standard deviation2948.023364
Coefficient of variation (CV)14.71662046
Kurtosis3691.495411
Mean200.3193174
Median Absolute Deviation (MAD)0
Skewness53.10106605
Sum5516794
Variance8690841.755
MonotocityNot monotonic
2021-04-25T02:14:16.669407image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
019891
58.8%
10211
 
0.6%
12204
 
0.6%
11180
 
0.5%
13168
 
0.5%
14153
 
0.5%
16128
 
0.4%
15123
 
0.4%
19116
 
0.3%
17114
 
0.3%
Other values (1442)6252
 
18.5%
(Missing)6311
 
18.6%
ValueCountFrequency (%)
019891
58.8%
1102
 
0.3%
256
 
0.2%
347
 
0.1%
425
 
0.1%
ValueCountFrequency (%)
2558481
< 0.1%
2069271
< 0.1%
1773201
< 0.1%
1419961
< 0.1%
785281
< 0.1%

Influenza Deaths
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct288
Distinct (%)1.0%
Missing4789
Missing (%)14.1%
Infinite0
Infinite (%)0.0%
Mean6.182471957
Minimum0
Maximum9004
Zeros26864
Zeros (%)79.4%
Memory size264.6 KiB
2021-04-25T02:14:17.214536image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile13
Maximum9004
Range9004
Interquartile range (IQR)0

Descriptive statistics

Standard deviation104.6396602
Coefficient of variation (CV)16.92521389
Kurtosis4219.748401
Mean6.182471957
Median Absolute Deviation (MAD)0
Skewness57.09514985
Sum179675
Variance10949.4585
MonotocityNot monotonic
2021-04-25T02:14:17.627075image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
026864
79.4%
1146
 
0.4%
10106
 
0.3%
1196
 
0.3%
1288
 
0.3%
288
 
0.3%
1361
 
0.2%
1558
 
0.2%
1454
 
0.2%
1753
 
0.2%
Other values (278)1448
 
4.3%
(Missing)4789
 
14.1%
ValueCountFrequency (%)
026864
79.4%
1146
 
0.4%
288
 
0.3%
345
 
0.1%
433
 
0.1%
ValueCountFrequency (%)
90041
< 0.1%
87761
< 0.1%
56861
< 0.1%
55341
< 0.1%
24361
< 0.1%

Pneumonia, Influenza, or COVID-19 Deaths
Real number (ℝ≥0)

HIGH CORRELATION
MISSING
SKEWED
ZEROS

Distinct2359
Distinct (%)9.1%
Missing7942
Missing (%)23.5%
Infinite0
Infinite (%)0.0%
Mean606.8093713
Minimum0
Maximum731429
Zeros13417
Zeros (%)39.6%
Memory size264.6 KiB
2021-04-25T02:14:18.459125image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q371
95-th percentile1472.6
Maximum731429
Range731429
Interquartile range (IQR)71

Descriptive statistics

Standard deviation8211.524317
Coefficient of variation (CV)13.53229648
Kurtosis3897.467243
Mean606.8093713
Median Absolute Deviation (MAD)0
Skewness54.44722616
Sum15721824
Variance67429131.6
MonotocityNot monotonic
2021-04-25T02:14:19.038429image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
013417
39.6%
10318
 
0.9%
11294
 
0.9%
12248
 
0.7%
13239
 
0.7%
14236
 
0.7%
15196
 
0.6%
16188
 
0.6%
18183
 
0.5%
17176
 
0.5%
Other values (2349)10414
30.8%
(Missing)7942
23.5%
ValueCountFrequency (%)
013417
39.6%
163
 
0.2%
241
 
0.1%
328
 
0.1%
429
 
0.1%
ValueCountFrequency (%)
7314291
< 0.1%
5577981
< 0.1%
4772431
< 0.1%
3582061
< 0.1%
2246411
< 0.1%

Footnote
Categorical

CONSTANT
HIGH CORRELATION
MISSING
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing14438
Missing (%)42.7%
Memory size264.6 KiB
One or more data cells have counts between 1-9 and have been suppressed in accordance with NCHS confidentiality standards.
19413 

Length

Max length122
Median length122
Mean length122
Min length122

Characters and Unicode

Total characters2368386
Distinct characters30
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOne or more data cells have counts between 1-9 and have been suppressed in accordance with NCHS confidentiality standards.
2nd rowOne or more data cells have counts between 1-9 and have been suppressed in accordance with NCHS confidentiality standards.
3rd rowOne or more data cells have counts between 1-9 and have been suppressed in accordance with NCHS confidentiality standards.
4th rowOne or more data cells have counts between 1-9 and have been suppressed in accordance with NCHS confidentiality standards.
5th rowOne or more data cells have counts between 1-9 and have been suppressed in accordance with NCHS confidentiality standards.
ValueCountFrequency (%)
One or more data cells have counts between 1-9 and have been suppressed in accordance with NCHS confidentiality standards.19413
57.3%
(Missing)14438
42.7%
2021-04-25T02:14:19.969935image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Histogram of lengths of the category
2021-04-25T02:14:20.083962image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
ValueCountFrequency (%)
have38826
 
10.5%
counts19413
 
5.3%
1-919413
 
5.3%
and19413
 
5.3%
or19413
 
5.3%
been19413
 
5.3%
nchs19413
 
5.3%
suppressed19413
 
5.3%
standards19413
 
5.3%
with19413
 
5.3%
Other values (8)155304
42.1%

Most occurring characters

ValueCountFrequency (%)
349434
14.8%
e271782
11.5%
n194130
 
8.2%
a194130
 
8.2%
d135891
 
5.7%
t135891
 
5.7%
s135891
 
5.7%
c116478
 
4.9%
o97065
 
4.1%
r97065
 
4.1%
Other values (20)640629
27.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1844235
77.9%
Space Separator349434
 
14.8%
Uppercase Letter97065
 
4.1%
Decimal Number38826
 
1.6%
Dash Punctuation19413
 
0.8%
Other Punctuation19413
 
0.8%

Most frequent character per category

ValueCountFrequency (%)
e271782
14.7%
n194130
10.5%
a194130
10.5%
d135891
 
7.4%
t135891
 
7.4%
s135891
 
7.4%
c116478
 
6.3%
o97065
 
5.3%
r97065
 
5.3%
i97065
 
5.3%
Other values (10)368847
20.0%
ValueCountFrequency (%)
O19413
20.0%
N19413
20.0%
C19413
20.0%
H19413
20.0%
S19413
20.0%
ValueCountFrequency (%)
119413
50.0%
919413
50.0%
ValueCountFrequency (%)
349434
100.0%
ValueCountFrequency (%)
-19413
100.0%
ValueCountFrequency (%)
.19413
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin1941300
82.0%
Common427086
 
18.0%

Most frequent character per script

ValueCountFrequency (%)
e271782
14.0%
n194130
10.0%
a194130
10.0%
d135891
 
7.0%
t135891
 
7.0%
s135891
 
7.0%
c116478
 
6.0%
o97065
 
5.0%
r97065
 
5.0%
i97065
 
5.0%
Other values (15)465912
24.0%
ValueCountFrequency (%)
349434
81.8%
119413
 
4.5%
-19413
 
4.5%
919413
 
4.5%
.19413
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2368386
100.0%

Most frequent character per block

ValueCountFrequency (%)
349434
14.8%
e271782
11.5%
n194130
 
8.2%
a194130
 
8.2%
d135891
 
5.7%
t135891
 
5.7%
s135891
 
5.7%
c116478
 
4.9%
o97065
 
4.1%
r97065
 
4.1%
Other values (20)640629
27.0%

Interactions

2021-04-25T02:13:51.218372image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:51.409206image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:51.583946image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:51.761566image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:51.940266image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:52.130519image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:52.296917image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:52.480411image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:52.647151image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:52.819402image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:53.002159image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:53.164805image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:53.345385image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:53.534354image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:53.724941image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:53.899117image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:54.083595image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:54.275734image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:54.439089image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:55.034580image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:55.195117image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:55.400630image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:55.589740image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:55.778626image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:56.010668image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:56.204901image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:56.402989image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:56.590480image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:56.792039image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:56.991534image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:57.176806image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:57.392726image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:57.574399image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:57.747688image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:57.933733image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:58.107925image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:58.287119image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:58.478681image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:58.660394image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:58.843590image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:59.022691image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:59.188695image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:59.377697image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:59.554940image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:59.717164image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:13:59.890549image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:14:00.093499image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:14:00.317796image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:14:00.476817image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:14:00.655406image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:14:00.824492image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:14:01.009762image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:14:01.201847image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:14:01.461353image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:14:01.667455image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
2021-04-25T02:14:01.893618image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Correlations

2021-04-25T02:14:20.234098image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-04-25T02:14:20.758238image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-04-25T02:14:21.107147image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-04-25T02:14:22.040787image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-04-25T02:14:22.354750image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-04-25T02:14:02.539854image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
A simple visualization of nullity by column.
2021-04-25T02:14:03.423649image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-04-25T02:14:04.017631image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-04-25T02:14:04.374405image/svg+xmlMatplotlib v3.4.1, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

sididpositioncreated_atcreated_metaupdated_atupdated_metametaData as ofStart DateEnd DateGroupYearMonthHHS RegionStatePlace of DeathAge groupCOVID-19 DeathsTotal DeathsPneumonia DeathsPneumonia and COVID-19 DeathsInfluenza DeathsPneumonia, Influenza, or COVID-19 DeathsFootnote
0row-xrtt.u63m-petw00000000-0000-0000-985B-3AC768A0E7E101616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002021-03-20T00:00:00By TotalNaNNaN0United StatesTotal - All Places of DeathAll Ages526027.04035809.0453484.0255848.09004.0731429.0NaN
1row-xvvt_qzkw-rvt200000000-0000-0000-FD08-DDB30B29C9A901616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002021-03-20T00:00:00By TotalNaNNaN0United StatesTotal - All Places of Death0-17 years238.038250.0646.044.0179.01019.0NaN
2row-s9xs~pfcz_s4we00000000-0000-0000-DA88-303EA3BF193001616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002021-03-20T00:00:00By TotalNaNNaN0United StatesTotal - All Places of Death18-29 years1916.072834.02109.0850.0150.03313.0NaN
3row-rjn9~8pz5_tcjq00000000-0000-0000-FA1F-B0B6C8B6BC1C01616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002021-03-20T00:00:00By TotalNaNNaN0United StatesTotal - All Places of Death30-39 years5583.0103647.05088.02561.0318.08406.0NaN
4row-2ktj.5dff.a4re00000000-0000-0000-E7E4-D4897A0EF3A501616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002021-03-20T00:00:00By TotalNaNNaN0United StatesTotal - All Places of Death40-49 years15134.0156430.012934.07445.0494.021048.0NaN
5row-rjzn~8uab.sdrb00000000-0000-0000-DE94-04F2628A675B01616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002021-03-20T00:00:00By TotalNaNNaN0United StatesTotal - All Places of Death50-64 years78883.0659981.072258.041686.02128.0111258.0NaN
6row-emcy~xkxq_hr9z00000000-0000-0000-84DA-BC7EBB6B6B4C01616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002021-03-20T00:00:00By TotalNaNNaN0United StatesTotal - All Places of Death65-74 years115381.0810095.0104453.061572.01939.0159891.0NaN
7row-k4ns~4hrc_4xd600000000-0000-0000-EC33-3C1DCF81CEF601616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002021-03-20T00:00:00By TotalNaNNaN0United StatesTotal - All Places of Death75-84 years146309.0986336.0127975.074117.01955.0201853.0NaN
8row-wwyk.emea-2wei00000000-0000-0000-9333-A435A0084F3601616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002021-03-20T00:00:00By TotalNaNNaN0United StatesTotal - All Places of Death85 years and over162583.01208236.0128021.067573.01841.0224641.0NaN
9row-cy3u.a6nx.wtdf00000000-0000-0000-D713-E4051964DE7001616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002021-03-20T00:00:00By TotalNaNNaN0United StatesHealthcare setting, inpatientAll Ages342259.01257053.0336525.0206927.05686.0477243.0NaN

Last rows

sididpositioncreated_atcreated_metaupdated_atupdated_metametaData as ofStart DateEnd DateGroupYearMonthHHS RegionStatePlace of DeathAge groupCOVID-19 DeathsTotal DeathsPneumonia DeathsPneumonia and COVID-19 DeathsInfluenza DeathsPneumonia, Influenza, or COVID-19 DeathsFootnote
33841row-wy3r-htzw-wvu700000000-0000-0000-AD85-C10FB86FC6AF01616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002020-01-31T00:00:00By Month2020.01.07KansasOther0-17 years0.0NaN0.00.00.00.0One or more data cells have counts between 1-9 and have been suppressed in accordance with NCHS confidentiality standards.
33842row-kv4e_6kz5-a9gi00000000-0000-0000-055E-F7A56C74BF4401616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002020-01-31T00:00:00By Month2020.01.07KansasOther18-29 years0.016.00.00.00.00.0NaN
33843row-a54n_pyf6.t2s700000000-0000-0000-4E32-E93E22005AB201616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002020-01-31T00:00:00By Month2020.01.07KansasOther30-39 years0.011.0NaN0.00.0NaNOne or more data cells have counts between 1-9 and have been suppressed in accordance with NCHS confidentiality standards.
33844row-apdu_mjtw-yh4k00000000-0000-0000-8E7D-EE687DEF6FC601616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002020-01-31T00:00:00By Month2020.01.07KansasOther40-49 years0.011.00.00.00.00.0NaN
33845row-d5ym_ftah~gxsp00000000-0000-0000-CEE6-6E5523D292CD01616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002020-01-31T00:00:00By Month2020.01.01VermontPlace of death unknown40-49 years0.00.00.00.00.00.0NaN
33846row-qn8c~haza.hz4s00000000-0000-0000-DFA3-6C88E54F95C601616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002020-01-31T00:00:00By Month2020.01.01VermontPlace of death unknown50-64 years0.00.00.00.00.00.0NaN
33847row-45fu_6uq9.d3af00000000-0000-0000-0D4F-473835A443BD01616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002020-01-31T00:00:00By Month2020.01.01VermontPlace of death unknown65-74 years0.00.00.00.00.00.0NaN
33848row-9x5n~nept_enyu00000000-0000-0000-D817-2866AEF74D5D01616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-01-01T00:00:002020-01-31T00:00:00By Month2020.01.01VermontPlace of death unknown75-84 years0.00.00.00.00.00.0NaN
33849row-spfz-w7in.qeqs00000000-0000-0000-B212-1C12065D8B3801616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-08-01T00:00:002020-08-31T00:00:00By Month2020.08.05WisconsinOther0-17 years0.0NaN0.00.00.00.0One or more data cells have counts between 1-9 and have been suppressed in accordance with NCHS confidentiality standards.
33850row-hx26~enk2_g69b00000000-0000-0000-6674-15DFD35AA38D01616604699NaN1616604699NaN{ }2021-03-24T00:00:002020-08-01T00:00:002020-08-31T00:00:00By Month2020.08.05WisconsinOther18-29 years0.032.00.00.00.00.0NaN